Obshazard-bench - Multi-faceted Impact Quantification: leaderboard
The post-disaster stage of Obshazard-bench (National University of Defense Technology, Shanghai AI Laboratory and Shanghai Jiao Tong University, 2026): estimating event magnitude, humanitarian burden and socio-economic intensity from observations and indicators (2,465 samples). Task-aware soft score in percent, graded by distance to the recorded values; higher is better.
Metric: Normalized task score (%; x100 of the 0-1 task-aware soft score following the Earth AI protocol: categorical answers by semantic or lexical consistency, numeric and interval answers by distance to the ground truth; equal-weight aggregation across timesteps, subtasks, lifecycle stages and disaster categories; zero-shot, uniform prompts; raw satellite sounding streams with ground-station, disaster-record and socio-economic inputs; post-disaster magnitude deduction, humanitarian burden and socio-economic intensity estimation, 2,465 samples). Source: arxiv.org. Saturation forecast: Around December 2027. 4 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5.5 | 29.77 | #48 |
| 2 | Claude Opus 4.8 | 22.97 | #56 |
| 3 | Kimi K2.6 | 22.75 | #173 |
| 4 | Qwen 3.5 397B A17B | 15.91 | #242 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=obshazard-bench-multi-faceted-impact-quantification · How It Works · Data refreshed daily, snapshot 2026-11-12.